Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21822
Title: Modified principal component analysis : an integration of multiple similarity subspace models
Authors: Fan, Z
Xu, Y
Zuo, W
Yang, J
Tang, J
Lai, Z
Zhang, D 
Keywords: Feature extraction
Modified principal component analysis (MPCA)
Similarity measurement
Similarity subspace
Weak machine cell (WMC)
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2014, v. 25, no. 8, 6701361, p. 1538-1552 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: We modify the conventional principal component analysis (PCA) and propose a novel subspace learning framework, modified PCA (MPCA), using multiple similarity measurements. MPCA computes three similarity matrices exploiting the similarity measurements: 1) mutual information; 2) angle information; and 3) Gaussian kernel similarity. We employ the eigenvectors of similarity matrices to produce new subspaces, referred to as similarity subspaces. A new integrated similarity subspace is then generated using a novel feature selection approach. This approach needs to construct a kind of vector set, termed weak machine cell (WMC), which contains an appropriate number of the eigenvectors spanning the similarity subspaces. Combining the wrapper method and the forward selection scheme, MPCA selects a WMC at a time that has a powerful discriminative capability to classify samples. MPCA is very suitable for the application scenarios in which the number of the training samples is less than the data dimensionality. MPCA outperforms the other state-of-the-art PCA-based methods in terms of both classification accuracy and clustering result. In addition, MPCA can be applied to face image reconstruction. MPCA can use other types of similarity measurements. Extensive experiments on many popular real-world data sets, such as face databases, show that MPCA achieves desirable classification results, as well as has a powerful capability to represent data.
URI: http://hdl.handle.net/10397/21822
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2013.2294492
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